Valentino Constantinou vc1492a

THIS GIST WON'T BE UPDATED ANY MORE (24/10/18)

Follow the progress of this project here 3os.org Raspberry Pi 3 TOR Access Point Router Project

Network: Router RJ45 <--> Ethernet Port on Raspberry <--> TOR <--> Raspberry WIFI AC <--> WIFI CLIENT

# -- Download Rasbian Strech Lite from: https://www.raspberrypi.org/downloads/raspbian/

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yes, even hidden code blocks!

print("hello world!")

Howto Install clamav on OSX with brew

Note: on legacy intel system the path may be /usr/local/etc/clamav instead of /opt/homebrew/etc/clamav/

$ brew install clamav
$ cd /opt/homebrew/etc/clamav/
$ cp freshclam.conf.sample freshclam.conf

The repository for the assignment is public and Github does not allow the creation of private forks for public repositories.

The correct way of creating a private frok by duplicating the repo is documented here.

For this assignment the commands are:

Create a bare clone of the repository. (This is temporary and will be removed so just do it wherever.)

git clone --bare [email protected]:usi-systems/easytrace.git

Using Python 3 + Google Cloud Vision API's OCR to extract text from photos and scanned documents

Just a quickie test in Python 3 (using Requests) to see if Google Cloud Vision can be used to effectively OCR a scanned data table and preserve its structure, in the way that products such as ABBYY FineReader can OCR an image and provide Excel-ready output.

The short answer: No. While Cloud Vision provides bounding polygon coordinates in its output, it doesn't provide it at the word or region level, which would be needed to then calculate the data delimiters.

On the other hand, the OCR quality is pretty good, if you just need to identify text anywhere in an image, without regards to its physical coordinates. I've included two examples:

####### 1. A low-resolution photo of road signs

Get ClamAV running on Mac OS X (using Homebrew)

The easiest way to get the ClamAV package is using Homebrew

$ brew install clamav

Before trying to start the clamd process, you'll need a copy of the ClamAV databases.

Create a freshclam.conf file and configure as so

	import networkx as nx
	import plotly.graph_objects as go

	def plotly_DAG(nx_dag, node_description_dict):
	"""
	nx_dag: a networkX directed acyclic graph
	node_description_dict: a nested dictionary that contains node attributes to show on hover
	eg. {
	'node_A':{'property_1':'value_A_1', 'property_2':'value_A_2'},
	'node_B':{'property_1':'value_B_1', 'property_2':'value_B_2'}

	a4b.amazonaws.com
	access-analyzer.amazonaws.com
	account.amazonaws.com
	acm-pca.amazonaws.com
	acm.amazonaws.com
	airflow-env.amazonaws.com
	airflow.amazonaws.com
	alexa-appkit.amazon.com
	alexa-connectedhome.amazon.com
	amazonmq.amazonaws.com

	import numpy as np
	from numpy.linalg import norm, solve
	from scipy.spatial.distance import cdist
	from sklearn.neighbors import kneighbors_graph


	def phi(l, mu):
	return (mu * (np.sqrt(l) - 1)**2)

	#!/usr/bin/ruby

	# For an OO language, this is distinctly procedural. Should probably fix that.
	require 'json'

	details = Hash.new({})

	capture_params = [
	{ :name => "title", :message => "Enter project name." },
	{ :name => "url", :message => "Enter the URL of the project repository." },